Miro's notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the NFL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nflgame\n",
    "import numpy as np\n",
    "#games = nflgame.games(range(2009,2017))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "games = nflgame.games(range(2009,2017))\n",
    "#print len(games)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Looking play by play"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tackles by game time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa20c35d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I'm graphing something at least\n",
    "def getTacklesByTime(game, team='home'):\n",
    "    playKeys = game.data[team]['stats']['defense'].keys() #Times of defensive plays\n",
    "    tackles = []\n",
    "    for i in playKeys:\n",
    "        tackles.append(game.data[team]['stats']['defense'][i]['tkl'])\n",
    "    homeDP = [int(i.replace('-','')) for i in playKeys] \n",
    "\n",
    "    combo = sorted(zip(homeDP, tackles))\n",
    "    return zip(*combo)\n",
    "\n",
    "time, tackles = getTacklesByTime(games[100])\n",
    "time2, tackles2 = getTacklesByTime(games[100], team = 'away')\n",
    "plt.plot(time, tackles, '.')\n",
    "plt.plot(time2, tackles2, '.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Net yards as game progresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gets times of plays and the play objects themselves\n",
    "def timePlays(game):\n",
    "    times = []\n",
    "    plays = []\n",
    "    for play in game.drives.plays():\n",
    "        if play.data['time'] == '': continue\n",
    "        times.append(6000 - 1500*(4-int(play.data['qtr'])) - int(play.data['time'].replace(':','')))\n",
    "        plays.append(play)\n",
    "    return times, plays\n",
    "\n",
    "#Grabs a given stat from all of the plays in the game along with times, the plays themselves, and home/away index\n",
    "def grabStats(game, toGrab = 'ydsnet'):\n",
    "    times, plays = timePlays(game)\n",
    "    indexHome = []\n",
    "    indexAway = []\n",
    "\n",
    "    store = [None]*len(plays)\n",
    "    for i in range(0, len(plays)):\n",
    "        if(plays[i].team == game.home): indexHome.append(i)\n",
    "        else: indexAway.append(i)\n",
    "        \n",
    "        store[i] = plays[i].data[toGrab]\n",
    "    return store, times, plays, indexHome, indexAway"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graphing net yards for this SD-CIN game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winner is SD\n"
     ]
    },
    {
     "data": {
      "image/png": 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8BLwOfIVKi/RN59xUjTLNljf4/lvAEnJQj8BDwB8AM8H9JfhbFwd82cxGzWxr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b57b49750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "toAnalyze = games[220]\n",
    "\n",
    "yards, times, plays, indexHome, indexAway = grabStats(toAnalyze)\n",
    "\n",
    "\n",
    "plt.plot([times[i] for i in indexHome], [yards[i] for i in indexHome], '.')\n",
    "plt.plot([times[i] for i in indexAway], [yards[i] for i in indexAway], '+')\n",
    "\n",
    "#print(\"Average home yards per play is %f from %s\" % (np.mean([yards[i] for i in indexHome]), toAnalyze.home))\n",
    "#print(\"Average away yards per play is %f from %s\" % (np.mean([yards[i] for i in indexAway]), toAnalyze.away))\n",
    "print(\"Winner is %s\" % toAnalyze.winner)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def grabSpikes(game): #Returns times, yards, and whether it was a home or away\n",
    "    times, plays = timePlays(game)\n",
    "    indexHome = []\n",
    "    indexAway = []\n",
    "\n",
    "    store = [False]*len(plays)\n",
    "    for i in range(0, len(plays)):\n",
    "        if(plays[i].team == game.home): indexHome.append(i)\n",
    "        else: indexAway.append(i)\n",
    "        if(\"spike\" in plays[i].desc or \"stop the clock\" in plays[i].desc):\n",
    "            store[i] = True\n",
    "    return store, times, plays, indexHome, indexAway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "times, plays = timePlays(games[12])\n",
    "spikes = grabSpikes(games[12])[0]\n",
    "\n",
    "\n",
    "allTimes = []\n",
    "allSpikes = []\n",
    "for i in range(0, len(games)):\n",
    "    spikes, times, a, b, c = grabSpikes(games[i])\n",
    "    allTimes.extend(times)\n",
    "    allSpikes.extend(spikes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zcr0K+MooZ1zM43i7LDPIzbqX069W1cMA3dfndONz5Vy2/N3lgdPpnR2PTM7ucsedwCHg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b48b89c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(allTimes, allSpikes, '.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Makes sense. All the spikes occur at the end of games or halfs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantifying home team advantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "toCheck = games[54]\n",
    "drives = toCheck.drives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "yards = []\n",
    "home = []\n",
    "\n",
    "for j in range(len(games)):\n",
    "    toCheck = games[j]\n",
    "    drives = toCheck.drives\n",
    "    for i in drives:\n",
    "        yards.append(i.total_yds)\n",
    "        home.append(i.home)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "yards = np.array(yards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Home team drive yards mean 29.915055, sd: 28.207665\n",
      "Away team drive yards mean 28.608894, sd: 27.726298\n"
     ]
    }
   ],
   "source": [
    "print(\"Home team drive yards mean %f, sd: %f\" % (np.mean(yards[home]), np.std(yards[home])))\n",
    "print(\"Away team drive yards mean %f, sd: %f\" % (np.mean(yards[np.invert(home)]), np.std(yards[np.invert(home)])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Very simple predictions of game result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = []\n",
    "homeWin = []\n",
    "\n",
    "\n",
    "for game in games:\n",
    "    features.append(game.stats_home[0:10] + game.stats_away[0:10]) #+ (game.score_home,game.score_away))\n",
    "    homeWin.append(game.winner == game.home)\n",
    "    \n",
    "features = np.array(features)\n",
    "homeWin = np.array(homeWin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=1e-06, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "np.random.seed(12346)\n",
    "mask = np.full(len(features), False)\n",
    "mask[:1600] = True\n",
    "np.random.shuffle(mask)\n",
    "\n",
    "\n",
    "clf = linear_model.SGDClassifier(tol=.000001)\n",
    "clf.fit(features[mask],homeWin[mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success rate in predicting winner is 70.758929%\n"
     ]
    }
   ],
   "source": [
    "print(\"Success rate in predicting winner is %f%%\" % (100*clf.score(features[np.invert(mask)], homeWin[np.invert(mask)])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scores at Half"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoreDiff = []\n",
    "winners = []\n",
    "for game in games:\n",
    "    scoreAtHalfH = 0\n",
    "    for i in game.drives:\n",
    "        if i.result == \"End of Half\":\n",
    "                break\n",
    "        if i.home:\n",
    "            if i.result==\"Touchdown\":\n",
    "                scoreAtHalfH += 7\n",
    "            elif i.result==\"Field Goal\":\n",
    "                scoreAtHalfH += 3\n",
    "        else:\n",
    "            if i.result==\"Touchdown\":\n",
    "                scoreAtHalfH -= 7\n",
    "            elif i.result==\"Field Goal\":\n",
    "                scoreAtHalfH -= 3\n",
    "    scoreDiff.append(scoreAtHalfH)\n",
    "    winners.append(game.winner == game.home)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pibLrJB1w9/dL+qakr5a7UQBAetkUNTMktbv7HkkyszWSrpD0TKTmCklfzN9+QNJtZmbu\n7mXsVZK07fkD2rxnv2ZNHa8Lzz6t37iY2a2P66WDhzSparQ2tczTvVte0IYd+7SgYYLOPfOU2Hk+\ncdcWPdnxmmbUna67r5up1vW79MjOlzX//DPVsvC82HmTtQ0rHtEb3e9obGWFLpl2hjY+26W559To\n1iXT9bk1T8XGdS0PF84ztrKi8LgdK+f3q42e943udwqP62ht7ve1NX7pUR18q0dVY7I6+FZPrDb6\nnB2tzVp82ybt2Pu6Giaequ2dvy9aG32ezoOHitZGxzVjK9X1RrdqxlZq6y2XFq1NzkOx2mxG6unN\n/dm+qrno/EZ1tDb3e02LzW90rVwz86zYeMWDv4r1cOk3Nuq5V9/U+6pP1mM3zo2t0Xt+2RHrLzr3\na5fPHrDXPsl1luwpKvm+GEotwmCl8tfMrpI0392X5sd/I2mmuy+P1OzI13Tmx8/la14d7LxNTU3e\n1tY2pGa3PX9AH7tzs7p7elWZzWjF5edr5UM7C+PVS2cNujhntz4eC6Nk6GUrTL29rspsRh8445RY\nyNUmguz6OVMLAf+Ju7boF79598vMSOodpP/keYqpMOkdH3yM8ur7SyKNxY0TtXb73lS1tVWj9eqb\n3erOn7w38hom12Bj7bhBAz65zuprTtZvut4sjFddeUEhtJPvk09eVKfv/GJPqtpi7yGMDGa2zd2b\nStWl2XO3Ae5LxkyaGpnZMjNrM7O2rq6uFE8dt3nPfnX39KrXpbd7erVhx77YePOe/YM+9qVEqEbf\nVJLU844XzrNj7+tFH/vIzpcLt5/seC12rFg+JM9TTDLICfajK22wS9LGZ9Ov3ZcOHiqs0d7Ea5hc\ng8l1F5VcZ8+9+mZsvGHHvsLt5Pskul5L1RZ7D+H4kibcOyVNjoxrJSU/thRqzCwraZyk1xI1cvc7\n3L3J3ZtqamqG3OysqeNVmc2owqRR2YwWNEyIjWdNHT/oYydVjY6Nq8bEd6SyFVY4T8PEU4s+dv75\nZxZuz6g7PXas2IQmz1NMhRUfo7yyQ/i5sbnnpF+7k6pGF9ZoJvEaJtdgct1FJdfZ+6pPjo0XNEwo\n3E6+T6LrtVRtsfcQji9plvRWSfVmNsXMKiUtkbQuUbNO0rX521dJ+vnR2G+/8OzTtHrpLN3wkXO1\neuksXTPzrNi42D8nN7XMU23VaJly/1Te/oXLtOrKC/Th+mqtuvIC/XDZRYXzrF0+W3PqqzV6VEZz\n6qu1qWWerp8zVXXjT4ptyUjS3dfNjNXuaW3W2MoKSbm948WNE1V10igtbpyoTS3zYuOO1uZYj9HH\nPfeV5ljtc1+Jnzeqo7U59rV1tDYXgiMZIMnn7GhtVmPtOGUzpsbacSVro89TqjaqZmxl4c9StdGv\ns1RtXyhnM/nvHxSZ3+R5oq9p+6ri8xtdK7cumR4bJ3uorzlZGcttnWxqmVdYoz+6/uJYf9u/cFls\n7ovtuSfX2WM3zo31EN1HT75PWhael7qWLZlwlNxzlyQzWyjpVkkVkr7n7v9mZisltbn7OjMbLeke\nSdOV+8S+pO8bsIM5nD13ADjRpd1zT/PTMnL39ZLWJ+5bEbl9SNJHh9okAODo4DdUASBAhDsABIhw\nB4AAEe4AECDCHQAClOpHIY/KE5t1SXp+WJ58ZKuWNOj/tgEFzFNpzFFpx+Mcne3uJX+TbtjCHQMz\ns7Y0P8N6omOeSmOOSgt5jtiWAYAAEe4AECDCfeS5Y7gbOE4wT6UxR6UFO0fsuQNAgPjkDgABItxH\nCDP7upn92syeNrOfmFlV5NhN+YuP7zazy4azz+FW6mLtJyIzm2xmT5jZLjPbaWafzd9/upk9Zma/\nyf/J/89XuetCm9lTZvZQfjzFzLbk5+mH+f+1+XGPcB85HpPU4O4flPSspJskKX8x8iWSzpc0X9K3\n8xctP+GkvFj7iahH0o3ufp6kWZI+k5+XFkmPu3u9pMfzY0iflbQrMv6qpG/m5+mApOuGpasyI9xH\nCHf/qbv3XXdts3JXvJJyFx9f4+5/cvffSmpX7qLlJ6LCxdrdvVtS38XaT2juvs/d/zd/+w/KBdck\n5ebmB/myH0haPDwdjhxmViupWdKd+bFJ+itJD+RLgpknwn1k+jtJG/K3J0l6MXKsM3/fiYi5KMHM\n6pS7aM4WSWe4+z4p9xeApPcOX2cjxq2S/lnvXu54vKSDkQ9WwaypVBfrQHmY2c8knTnAoZvd/cF8\nzc3K/TN7dd/DBqg/UX/EibkowszGSvqxpM+5++u5D6XoY2aXS3rF3beZ2dy+uwcoDWJNEe7HkLtf\nUuy4mV0r6XJJ8yLXoE1zgfITBXMxCDMbpVywr3b3/8rf/Tszm+Du+8xsgqRXhq/DEeFDkhblLxs6\nWtKpyn2SrzKzbP7TezBrim2ZEcLM5kv6F0mL3P2PkUPrJC0xs/eY2RRJ9ZKeHI4eR4A0F2s/4eT3\nje+StMvd/z1yKHrh+mslPXisextJ3P0md6919zrl1s7P3f1jkp6QdFW+LJh54peYRggza5f0Hkn7\n83dtdvfr88duVm4fvke5f3JvGPgs4RvoYu3D3NKwM7PZkv5b0q/07l7y55Xbd79f0lmSXpD0UXd/\nbViaHGHy2zL/6O6Xm9lU5b45f7qkpyR93N3/NJz9lQPhDgABYlsGAAJEuANAgAh3AAgQ4Q4AASLc\nASBAhDsABIhwB4AAEe4AEKD/B8KIEgXhxozdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b57b49810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scoreDiff, winners, '.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}